Moore's Law continues to drive higher performance with smaller circuit features. Aggressive technology scaling has introduced new variation sources and made process variation control more difficult. For optical lithography, manufacturability is roughly defined by the k1 factor from the Rayleigh equation. Beyond the 45 nm CMOS technology node, even using a high-end optical exposure system such as immersion lithography with higher numerical aperture (NA), it is necessary to have a k1 factor lower than 0.35. The primary risk posed by lower k1 is the likelihood of degradation of patterning fidelity on VLSI circuits. Lower k1 could decrease patterning fidelity and result in generation of many “hotspots.” A hotspot is an actual device pattern which has relatively large critical dimension (CD) and image errors with respect to on-wafer targets. The critical dimension (CD), also known as line width or feature width, is the size (width) of a feature printed in resist, measured at a specific height above the substrate. Under ultra-low k1 conditions (k1<0.3), in particular, many hotspots may arise anywhere. Hotspots can form under a variety of conditions such as the original design being unfriendly to the resolution enhancement technique (RET) that is applied to the chip, pattern combinations unanticipated by rule-based optical proximity correction (OPC), or inaccuracies in model-based OPC. When these hotspots fall on locations that are critical to the electrical performance of a device, they can reduce the yield and performance of the device. It is therefore necessary to detect hotspots earlier in the layout design flow.
One known detection method for critical patterns (hotspots) includes using a design rule check (DRC) tool. The approach is a rule-based detection which generates lookup tables with line and space parameters. However, for more complex patterns, the number of layout pattern parameter required to enable detection increases. As a result, the speed advantage of the rule-based approach is reduced.
Thus, the simulation-based approach has occupied the mainstream and has been able to detect hotspots accurately. Further, software solutions running on customized hardware platforms have been developed so that aerial image simulation can be carried out quickly. However, hotspots can be changed according to process conditions. The accuracy requirement for hotspot detection strongly depends on qualified optical and process models. Model generation corresponding to process variation represents a significant overhead in terms of validation, measurement and parameter calibration.